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Proceedings Paper

Automated detection of pulmonary nodules in helical computed tomography images of the thorax
Author(s): Samuel G. Armato; Maryellen Lissak Giger; Catherine J. Moran; Heber MacMahon; Kunio Doi
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Paper Abstract

We are developing a fully automated method for the detection of lung nodules in helical computed tomography (CT) images of the thorax. In our computerized method, gray-level thresholding is used to segment the lungs from the thorax region within each CT section. A rolling ball operation is employed to more accurately delineate the lung boundaries, thereby incorporating peripheral nodules within the segmented lung regions. A multiple gray-level thresholding scheme is then used to capture nodules by creating a series of binary images in which a pixel is turned `on' if the corresponding image pixel has a gray level greater than the selected threshold. Groups of contiguous `on' pixels are identified as individual signals. To distinguish nodules from vessels, geometric descriptors are calculated for each signal detected in the series of binary images. The values of these descriptors are input to an artificial neural network, which allows for the elimination of a high percentage of false-positive signals.

Paper Details

Date Published: 24 June 1998
PDF: 4 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310968
Show Author Affiliations
Samuel G. Armato, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Catherine J. Moran, Univ. of Chicago (United States)
Heber MacMahon, Univ. of Chicago (United States)
Kunio Doi, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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